Search results for ""Author David J Miller""

Sweating Together: How Peloton Built a Billion Dollar Venture and Created Community in a Digital World by David J. Miller, PhD (#ChicagoBorn) The ultimate front row look at the meteoric rise of Peloton, one of the hottest consumer and fitness brands in the world. In Sweating Together Miller brings readers directly into the center of the sweat soaked, adrenaline fueled, NYC phenomena that is Peloton and provides a first-hand account of the rise of one of the most important ventures of tomorrow’s economy. In 2012 John Foley and a group of co-founders launched Peloton, an interactive fitness and media company. In less than 10 years the company would be worth billions, disrupt the fitness industry and create a rabid, life changing community of members using sweat to span the digital and physical worlds. Join Peloton fanatic and George Mason University entrepreneurship professor David J. Miller (#ChicagoBorn on the Peloton platform) as he dives deep into the people, business models and stories behind the ascent of Peloton. From well-being, social media and gamification to the role of physical space in a digital world, talent retention and community building, there is no better venture for understanding our ever-expanding innovation fueled, well-being economy than Peloton. Miller unwittingly became a Peloton addict and spent thousands of hours sweating and growing relationships with Peloton members; he interviewed founders John Foley and Tom Cortese as well as other senior Peloton leaders, and Peloton celebrity instructors Robin Arzon, Matt Wilpers, Jenn Sherman and Jess King. Join Miller and race into the future with Peloton

Read more less

Providing a logical framework for student learning, this is the first textbook on adversarial learning. It introduces vulnerabilities of deep learning, then demonstrates methods for defending against attacks and making AI generally more robust. To help students connect theory with practice, it explains and evaluates attack-and-defense scenarios alongside real-world examples. Feasible, hands-on student projects, which increase in difficulty throughout the book, give students practical experience and help to improve their Python and PyTorch skills. Book chapters conclude with questions that can be used for classroom discussions. In addition to deep neural networks, students will also learn about logistic regression, naïve Bayes classifiers, and support vector machines. Written for senior undergraduate and first-year graduate courses, the book offers a window into research methods and current challenges. Online resources include lecture slides and image files for instructors, and software for early course projects for students.

Read more less